1 Iranian Research Organization for Science and Technology (IROST), Iran 2 Society of Young Researchers, Shahid Bahonar University of Kerman, Iran
Society of Young Researchers, Shahid Bahonar University of Kerman, Iran 3 Department of Petroleum Engineering, Shahid Bahonar University of Kerman, Iran
Department of Electrical Engineering, Shahid Bahonar University of Kerman, Iran 5 Energy and Environmental Engineering Research Centre (EERC), Shahid Bahonar University of Kerman, Iran
The late detection of the kick (the entrance of underground fluids into oil wells) leads to oil well blowouts. It causes human life loss and imposes a great deal of expenses on the petroleum industry. This paper presents the application of adaptive neuro-fuzzy inference system designed for an earlier kick detection using measurable drilling parameters. In order to generate the initial fuzzy inference system, subtractive clustering is utilized. The training set contains 50 data samples and there are 362 data samples for testing the proposed method. Also, ANFIS structure is examined at different radii (the parameter of subtractive clustering). Different conformations are tested to get the earliest detection and the lowest false alarms while facing kick. Eventually, ANFIS verifies the danger exposure depth of about 28.6 meters before the depth that the kick was sensed by crew. Such an assessment gives the rig crew enough time to prepare for the danger and stop the operation before being exposed to high pressure zones.